The investigation of solar-like oscillations for probing the star interiors has encountered a tremendous growth in the last decade. For ground based observations the most important difficulties in properly identifying the true oscillation frequencies of the stars are produced by the gaps in the observation time-series and the presence of atmospheric plus the intrinsic stellar granulation noise, unavoidable also in the case of space observations. In this paper an innovative neuro-wavelet method for the reconstruction of missing data from photometric signals is presented. The prediction of missing data was done by using a composite neuro-wavelet reconstruction system composed by two neural networks separately trained. The combination of these two neural networks obtains a "forward and backward" reconstruction. This technique was able to provide reconstructed data with an error greatly lower than the absolute a priori measurement error. The reconstructed signal frequency spectrum matched the expected spectrum with high accuracy.

Innovative Second-Generation Wavelets Construction With Recurrent Neural Networks for Solar Radiation Forecasting

CAPIZZI, GIACOMO;NAPOLI, CHRISTIAN;
2012-01-01

Abstract

The investigation of solar-like oscillations for probing the star interiors has encountered a tremendous growth in the last decade. For ground based observations the most important difficulties in properly identifying the true oscillation frequencies of the stars are produced by the gaps in the observation time-series and the presence of atmospheric plus the intrinsic stellar granulation noise, unavoidable also in the case of space observations. In this paper an innovative neuro-wavelet method for the reconstruction of missing data from photometric signals is presented. The prediction of missing data was done by using a composite neuro-wavelet reconstruction system composed by two neural networks separately trained. The combination of these two neural networks obtains a "forward and backward" reconstruction. This technique was able to provide reconstructed data with an error greatly lower than the absolute a priori measurement error. The reconstructed signal frequency spectrum matched the expected spectrum with high accuracy.
2012
Neural Networks; Wavelet Theory; Artificial intelligence
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11769/34641
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